Recent developments have demonstrated that particle filtering is an emerging and powerful methodology, using Monte Carlo methods, for sequential signal processing with a wide range of applications in science engineering. It has captured the attention many researchers various communities, including those processing, statistics econometrics. Based on concept importance sampling use Bayesian theory, particularly useful dealing difficult nonlinear non-Gaussian problems. The underlying principle...

A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization objects wireless sensor networks [1] and Internet Things [2]; multiple source reconstruction electroencephalograms [3]; power spectral density for speech enhancement [4]; inference genomic [5]. Within Bayesian framework, these problems are addressed by constructing posterior probability distributions unknowns. The posteriors combine...

We address the problem of approximating posterior probability distribution fixed parameters a state-space dynamical system using sequential Monte Carlo method. The proposed approach relies on nested structure that employs two layers particle filters to approximate measure static and dynamic state variables interest, in vein similar recent “sequential square” (SMC$^{2}$) algorithm. However, unlike SMC$^{2}$ scheme, technique operates purely recursive manner. In particular, computational...

Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means sets weighted particles. While the convergence filter is guaranteed when number particles tends infinity, quality approximation usually unknown but strongly dependent on In this paper, we propose a novel method for assessing particle online manner, as well simple scheme adaptation based assessment. The sequential comparison between actual observations and their predictive...

This article presents a linearly constrained constant modulus approach for the blind suppression of multiuser interferences in direct-sequence code division multiple access systems. The method performs same as minimum mean square error receivers and outperforms existing approaches because it only requires rough estimate desired user timing.

In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require mathematical representation the dynamics system evolution, together with assumptions probabilistic models. this paper, we present new class that do not assume explicit forms probability distributions noise in system. As consequence, proposed techniques are simpler, more robust, flexible than standard filters. Apart from theoretical...

Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider systems consisting of interconnected nodes. The state variables each node evolve time according a set (possibly nonlinear and chaotic) differential equations. One the plays role master system drives second network by way an open-plus-closed-loop (OPCL) scheme. Starting from simple analysis synchronization...

Model assessment is a fundamental problem in science and engineering it addresses the question of validity model light empirical evidence. In this paper, we propose method for dynamic nonlinear models based on predictive cumulative distributions data Kolmogorov-Smirnov statistics. The technique generation discrete random variables that come from known distribution if entertained correct. We provide simulation examples demonstrate performance proposed method.

The literature in engineering and statistics is abounding techniques for detecting properly processing anomalous observations the data. Most of these have been developed framework static models it only recent years that we seen attempts address presence outliers nonlinear time series. For a target tracking problem described by state-space model, propose online detection including an outlier step within standard particle filtering algorithm. implemented test involving statistic predictive...

Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of a posteriori probability measures generated by state-space dynamical models. At any given time $t$, SMC method produces set samples over state space system interest (often termed "particles") that is used to build discrete and random posterior distribution variables, conditional on sequence available observations. One potential application methodology...

This paper introduces a novel blind equalization algorithm for frequency-selective channels based on Bayesian formulation of the problem and sequential importance sampling (SIS) technique. SIS methods rely building Monte Carlo (MC) representation probability distribution interest that consists set samples (usually called particles) associated weights computed recursively in time. We elaborate this principle to derive algorithms perform maximum posteriori (MAP) symbol detection without...